Data federation can combine data without having to move the data from the original sources, accelerating the data integration process.

“Data virtualization or federation has achieved a significant role in enterprise data management strategies because it allows data integration teams to quickly create virtually integrated data sets for many purposes – BI, CRM, MDM, etc.,” Imhoff explained in a release.

To ensure proper adoption, the authors warn about potential pitfalls such as insufficient monitoring of potential impact on source systems and not fully determining data virtualization infrastructure requirements. In addition, Imhoff and White caution against using data virtualization or federation for inappropriate scenarios such as the creation of virtual data warehouses, large-scale real-time analytics or where highly complex, multi-step transformations and aggregations are required.

Other mistakes to avoid include: failures such as not properly understanding the opportunities to gain value from data virtualization, including underutilizing it in data management architectures; not applying it to prototyping and other activities it does better than more traditional approaches; not defining shared business views of source data; and limiting its use to relational data only.